从灵感到转型:Apator 如何打造未来工厂
电子制造存在一个悖论:流程越自动化,仍依赖人工经验的环节反而越显重要。Apator 围绕这一思路推进未来工厂建设,涉及自动化与人工经验的分工。原文未给出具体工厂地点、投资金额、产能数字或时间节点,也未提及元器件型号与供应链影响。
原文深度解读
本文以波兰计量设备与系统制造商 Apator(历史可追溯至 1949 年)为例,讲述其从参观另一家波兰制造商 Fideltronik 位于 Sucha Beskidzka 的工厂获得启发,进而引入由波兰公司 AiRob 开发、基于人工智能的图像检测系统,用于在工位端支持操作员进行质量检查。文章涉及 Apator 运营总监 Sławomir Migała 与 AiRob 首席执行官 Piotr Owczarek 的访谈内容,关键数字包括:首个试点在 7 个生产工位安装摄像头、约一年后评估结果、三班制(常接近全天候)生产。文章强调先有问题、后有技术,目标是实现 100% 检测覆盖而不进行大规模资本投资,并让系统随组织需求持续演进。与电子元器件供应链的关系在于:该案例展示了电子制造环节中自动化检测、操作员辅助与生产数据采集的落地方式,可能影响制造端质量管控与数据透明度,但原文未提及具体元器件型号、供应商、产能数字或元器件采购与流通影响。
- 原文指出,产品复杂度上升、客户要求提高以及维持最高质量标准的持续压力,正迫使制造商在不牺牲质量的前提下重新思考运营组织方式,并越来越多地探索基于人工智能的解决方案及其他自动化和生产支持形式。
- Apator 团队在未预设需求清单或特定供应商的情况下,参观了另一家波兰制造商 Fideltronik 位于 Sucha Beskidzka 的工厂,主要目的是获得启发并对比自身实践;在此过程中接触到由另一家波兰公司 AiRob 正在实施并持续开发的解决方案。
- Apator 运营总监 Sławomir Migała 表示,其寻找的是帮助操作员在工位端把质量做进流程的方案,要求速度快、对工程师参与需求最小、易于创建检测计划,同时不损害生产吞吐量;原文强调起点不是 AI 本身,而是具体运营挑战。
- 原文举例称,在现有产量下全自动化该产线不具经济性,但客户期望每件产品都经过自动化检查以确认所需组件齐全;因此 Apator 决定在每个工位安装系统,从而实现 100% 检测覆盖且无需重大资本投资。
- 试点阶段首先在 7 个生产工位安装摄像头,随后数月持续观察系统表现及不同生产区域的使用方式;约一年后评估结果发现,三个主要实施区域发展不同,各团队使用方式和节奏不同,因此需要专人负责在全组织范围内标准化该方案。
- 原文称,随着项目推进,新想法不再仅来自项目团队,生产经理开始主动提出新的应用;系统角色也从质量检测扩展到收集生产数据,可回溯数月前产品的整个装配过程,查看每道工序由谁执行、不符合项出现在哪里以及如何解决。
供应链影响
- 若类似工位端 AI 图像检测在电子制造中推广,可能提升装配完整性检查覆盖率,从而可能降低因缺件或错件导致的返工与客户投诉风险,但实际效果取决于具体产线、产量与实施范围。
- 原文显示该系统在现有产量下替代全自动化产线以实现 100% 检测覆盖,这可能意味着部分制造环节对全自动产线资本支出的依赖降低,但是否影响设备与自动化元器件需求取决于各工厂的经济性判断。
- 系统采集每道工序、每次修正与每次检测的数据并可回溯数月前产品装配过程,可能提升制造端过程透明度与追溯能力,进而可能影响质量审核、客户审计与问题定位效率,但数据可用性与标准化程度取决于组织是否建立统一标准与责任结构。
- 原文提到操作员反馈系统使工作压力降低、可在工位即时纠正问题,这可能有助于稳定三班制下的人工质量表现,但能否转化为整体质量与交付改善取决于培训、流程标准化与持续改进机制。
- 原文未提及具体元器件型号、供应商、产能数字或采购与流通影响,因此对电子元器件供应链上游或分销环节的直接影响无法从原文事实直接推出。
系统解读,仅作为判断线索,不构成备货、出货、涨价或投资建议。
从灵感到转型:Apator 如何打造未来工厂
与此同时,日益增长的产品复杂度、越来越苛刻的客户要求以及维持最高质量标准的持续压力,正迫使制造商重新思考如何组织其运营。而且他们必须在不让质量受损的前提下做到这一点。恰恰相反:目标是进一步提升质量。
因此,制造商越来越多地探索基于人工智能的解决方案,以及其他形式的自动化和生产支持,这并不令人意外。然而,最有趣的方面未必是技术本身。更能说明问题的是,组织最初是如何做出实施该技术的决定的。
关于 AI 的讨论往往集中在投资回报、自动化和降低成本上。对于实施这些技术在实践中究竟意味着什么,关注则少得多。是什么促使一家公司迈出第一步?什么决定了一个项目能否成功?又在什么时候,一项技术不再只是实验,而成为工厂日常运营中不可或缺的一部分?
Apator 的故事说明了这些问题的答案早在公司投资新解决方案之前就已开始成形。Apator 是一家波兰计量设备与系统制造商,其历史可追溯至 1949 年。在这个案例中,一切始于对另一家制造工厂的访问。
理解真实需求是关键
Apator 团队访问了另一家波兰大型制造商 Fideltronik 位于 Sucha Beskidzka 的工厂,了解该公司如何使用技术支持电子制造。然而,他们并没有带着预先定义的需求清单或特定供应商前来。主要目标是获得启发,并将自身经验与另一家非常成功的制造商的做法进行比较。
正如 Apator 运营总监 Sławomir Migała 在接受 Evertiq 采访时所解释的,这种做法正变得越来越必要。
Sławomir Migała | © Apator
“在探索新的技术领域时,你必须认识到,你无法从自己的工厂内部看到所有可能性。这就是为什么我们要与其他人对标,研究最佳实践,并评估它们如何契合我们自身的需求。”
这一观察概括了贯穿我们整个对话的一种理念。在一个技术发展速度快于生产管理教科书更新速度的世界里,竞争优势越来越不属于那些已经拥有所有答案的人,而属于那些知道如何提出正确问题的人。
正是在访问 Fideltronik 期间,Apator 团队接触到另一家波兰公司 AiRob 正在实施并进一步开发的解决方案。吸引他们注意的,并不仅仅是它使用了人工智能这一如今常被视为现代制造必备部分的技术。原因要实际得多。
“我们当时在寻找能够帮助操作员在工位端就把质量做进流程的解决方案,”Migała 回忆道。“我们想要一个速度快、对工程师参与需求最小、易于创建检测计划,同时又不损害生产吞吐量的系统。当我们看到该解决方案实际运行后,我们意识到它满足了我们许多需求。”
那一刻,技术不再是一个抽象概念,而成为对具体制造挑战的实际回答。
问题在先,技术在后
人们很容易假设,AI 的采用始于对新技术可能性的热情,而这种热情受到制造商拥抱人工智能日益增长的压力推动。然而,我们与 Apator 的对话揭示了恰恰相反的情况。
对这家波兰制造商而言,起点不是 AI 本身,而是一个具体的运营挑战。在某些流程中,Apator 需要一种更有效的方式,在质量检查期间为操作员提供支持。与此同时,并非每个流程都值得为全自动化或对现有产线进行重大改造而付出成本。而且值得记住的是,相关产品高度精密且敏感。
因此,Apator 正在寻找一种方式,在装配期间为操作员提供更好的支持,提高流程可靠性,并在全自动化根本不具备经济性的领域降低出错风险。
“我们当时在寻找一种解决方案,帮助操作员在工位端就把质量做进产品。我们想要某种易于实施、所需工程资源最少,同时又不损害生产吞吐量的东西。”
该公司的一条新产线提供了一个特别好的例子。
鉴于现有产量,对该产线进行全自动化在经济上并不可行。然而,客户期望每一件产品都经过自动化检查,以在明确定义的检查范围内确认所有所需组件均已存在。
“在我们当前的产量下,对产线进行全自动化根本不合算。但在一次审核中,客户问了我们一个非常具体的问题:‘你们如何保证每件产品都是完整的?’那时我们决定在每个工位安装该系统。这使我们能够在不进行重大资本投资的情况下实现 100% 检测覆盖,”Sławomir Migała 解释道。
正是在这里,实施技术与解决问题之间的区别变得尤为清晰。团队寻找的不是市场上最先进的解决方案。他们寻找的是一种能适配其现有生产流程的工具。
“我们不想引入孤立的解决方案。我们的目标是创建一个支持整体生产的综合系统。我们实行三班制,常常接近全天候运转。我们知道,人的注意力水平在一天中会波动。这就是为什么我们把这种支持视为生产流程的自然组成部分。”
这一区别很重要。目标从来不是取代操作员或削弱他们的角色。恰恰相反:目标是创造一个有助于保持一贯高质量标准的工作环境,即使在那些全自动化既不切实际、在经济上也不合理的流程中也是如此。
从灵感到第一步
访问归来后,Apator 并没有急于进行大规模推广。相反,公司开始分析哪些流程真正最能从新解决方案中受益。团队还着手确定该技术在哪里能带来最大价值,以及哪些领域应优先进行初始试点。来自 AiRob 的专门团队与 Apator 密切合作,帮助回答这些问题。
正如 Migała 所强调的,合作伙伴的专业能力发挥了重要作用。AiRob 不仅仅提供技术,还帮助 Apator 确定哪些领域最有可能通过实施取得切实成果。
结果,第一批摄像头安装在 7 个生产工位上。但这只是项目的开始。更大的挑战在于确定该技术能否成为日常生产运营的自然组成部分。
真正的问题不再只是系统是否有效。而是组织能否学会有效利用它。
试点项目
系统上线后的最初几周,既没有戏剧性的公告,也没有立即扩大项目的决定。
在 Apator,团队明白答案需要时间。公司没有急于大规模推广,而是选择了一个试点项目。团队与 AiRob 一起确定了该解决方案能带来最大价值的领域,并安装了首批配备基于图像检测系统的工位。
在接下来的几个月里,项目稳步推进。团队不仅监测系统性能,还监测它在不同生产区域中的使用方式。只有通过长期观察,他们才能确定该解决方案是否真正成为生产流程的一部分,还是仅仅作为工位上的另一台设备而存在。
大约一年后,是时候评估结果了。
正如 Sławomir Migała 回忆的那样,这时团队开始得出短期测试永远无法得出的结论。
“我们有三个主要领域实施了该系统,而每个领域的发展都略有不同。我们看到各个团队以不同方式使用该解决方案,工作节奏也不同。这向我们表明,我们需要有人负责在全组织范围内对该解决方案进行标准化。”
乍看之下,这似乎只是纯粹的组织问题。实际上,它标志着项目下一阶段的开始。各个团队的不同经验表明,进一步发展将需要共同标准和明确界定的责任。
“今天,我们已经建立了专门的结构,由专人负责开发和标准化该系统。我们还看到越来越多新的需求以及关于该解决方案如何使用的想法。我们不想要一系列孤立的实施。我们的目标是创建一个支持整体生产的综合系统。”
因此,该项目开始超越单个实施。
也许这种转变最清晰的迹象是,新想法不再仅来自负责该项目的团队。越来越多的生产经理自己主动提出该技术的新应用。
这可以说是真正的转折点,表明该系统已开始赢得更广泛组织的信任。
“今天,生产经理会带着他们自己对进一步实施的想法来找我们。他们能看到数据,能看到好处,也知道哪些流程最能从该解决方案中受益。我认为这是该系统已成为我们工作方式自然组成部分的最佳证明。”
在许多组织中,一旦新技术上线运行,转型就被认为完成了。然而在 Apator,那只是开始。
好的技术释放人的潜力
尽管实施的最初目标是支持质量检查,但随着时间推移,Apator 开始认识到该解决方案更广泛的影响。在某些流程中,公司已识别出产品在装配不完整的情况下流出的风险,这需要在工位端直接解决。越来越清楚的是,该技术也在改变人们如何利用自己的时间和专业知识。
这是关于人工智能的讨论中相对较少受到关注的一个方面,尽管它曾是这项技术最初的承诺之一:帮助人们充分发挥潜力。
在许多制造公司中,质量问题通过额外培训、新的作业指导书、工位布局变更或产品设计修改来解决。每一项措施都需要时间、资源以及熟练人员的参与。
据 Sławomir Migała 称,操作员辅助技术的进步如今使人们能够以不同方式应对这些挑战。
“过去,每当出现质量问题时,我们会考虑是提供额外的操作员培训、修订作业指导书,还是重新设计产品以消除出错的可能性。但所有这些措施都需要时间,并需要专家参与。今天,我们可以更快地解决其中一些问题。”
通过接管重复性检查任务,该技术使工程师和质量专家能够将专业知识集中在能带来最大价值的地方。
“这使我们能够把更多时间投入到真正需要知识和经验的任务上。我们可以不再专注于反复出现的问题,而是致力于改进流程、分析数据和开发进一步的改进。”
这种方法与持续流程改进的理念高度一致。一致的质量控制本身不是目的。它提供了一个基础,组织可以在此基础上构建下一阶段的发展。
正如 Migała 在我们的对话中强调的,这正是为什么该项目从一开始就被视为一项长期投资。
“我们不想为个别问题开发个别解决方案。我们的目标是构建一个能够支持我们制造运营的环境,无论我们的流程如何演变。”
一双数字眼睛
实施过程中最有趣的发现之一,是该系统对操作员日常工作产生的影响。
今天,关于人工智能的讨论常常围绕新技术是否会取代人工,以及它们能产生多少财务价值。然而在 Apator,对话从一开始就走向了不同方向。问题是技术如何帮助人们更有信心、压力更小、负担更轻地完成工作。
正是在这种背景下,Migała 将该系统描述为“一双数字眼睛”。
“没有人能在八小时班次中始终保持同样的注意力水平。这完全正常。所以我们把这个系统看作额外的一双眼睛,支持操作员,帮助他们在产品离开工位之前发现潜在错误。”
这种方法也改变了人们对质量检查的看法。传统上,错误往往只在生产流程末端才被发现,此时纠正意味着额外工作、延误或返工成品。而在这里,纠正措施可以立即采取,操作员在执行任务的那一刻就能收到反馈。
Piotr Owczarek | © AiRob
正如 AiRob 首席执行官 Piotr Owczarek 所解释的,这种即时反馈正是该解决方案区别于传统质量检查的地方。
“如果系统检测到缺少组件或其他不符合项,操作员可以立即纠正。无需等待最终质量检查或调查客户投诉。问题就在发生的地方得到解决,”Owczarek 解释道。
随着时间推移,这不仅开始影响流程质量,也影响工作环境和生产现场的氛围。
“对我来说非常重要的一点是,操作员不再把质量检查视为对自己表现的评估。没有检查员或审核员站在他们身后。相反,他们拥有一个帮助他们正确完成工作的工具。这确实改变了人们对质量的看法。”
AiRob 在其他实施中也观察到了类似效果。
“有几次,操作员告诉我们,该解决方案只是让他们的工作压力更小。他们知道,如果他们忽略了什么,系统会立即标记出来。这给他们带来了真正的安心感。”
这是那种难以在电子表格中体现的好处之一,却可能对整个团队的工作场所文化和工作质量产生深远影响。
一个随组织演进的解决方案
在许多项目中,让系统上线运行实际上标志着实施过程的结束。客户收到一个现成的工具,任何后续开发基本上都在使用该工具的工厂之外独立进行。然而在 Apator 与 AiRob 的案例中,生产现场获得的经验已成为该解决方案自身持续发展的驱动力。
正如 Piotr Owczarek 所指出的,每个制造工厂都会为流程带来自己的要求和生产特征。
“每个客户的工作方式都略有不同,每个制造运营也都有自己特定的要求。当然,我们带着现有解决方案进来,但很快就会发现某些流程需要新功能或不同方法。而那时,我们工作中最有趣的部分就开始了。”
一个很好的例子是如今可能看起来像系统显而易见组成部分的一系列功能,但实际上它们是根据 successive 实施过程中识别出的需求而引入的。
“最初,系统无法读取二维码或文本。是我们的客户向我们表明,他们的流程需要这些能力。随着每一次新的实施,系统逐渐演进。”
Sławomir Migała 对这段合作关系的看法也大致相同。在谈到 AiRob 时,他特别强调该团队愿意理解 Apator 的生产流程,并共同寻找解决方案。
“对我来说真正重要的是,我们与之合作的人真心想理解我们的制造运营。他们不会带着已经拥有所有答案的假设前来。他们先倾听,然后提出解决方案,并与我们并肩工作,看看这些解决方案在实践中表现如何。”
在这种模式下,生产流程不必被迫适应现成工具的局限。相反,生产现场出现的新需求成为系统进一步开发的起点。
让数据发挥作用
最初,该系统主要旨在支持操作员并协助质量检查。然而随着时间推移,它的作用开始远远超出验证装配准确性。越来越重要的不仅是检查结果,还有系统收集的数据。每件产品、每道执行的工序、每次进行的纠正以及每次成功的检查,单独来看只能提供有限洞察。然而,把这些记录放在一起,就能构建出整个生产流程的详细图景。
“今天,我们从更广阔的视角看待这个解决方案。它不再只是一个质量检查系统。它已成为我们流程的另一个宝贵信息来源。我们对生产现场发生的事情了解得越多,就越容易对未来改进做出明智决策,”Sławomir Migała 说。
对 Piotr Owczarek 而言,这代表了此类技术最自然的发展方向之一。
“每道工序都会留下痕迹。我们可以回溯几个月前制造的产品,重建其整个装配过程。我们可以看到每道工序由谁执行、不符合项出现在哪里以及如何解决。这为流程分析开辟了全新可能性。”
对 Apator 来说,这意味着能够基于实际生产数据做出决策,而不是仅依赖观察或孤立事件。而正是这种基础,使数字化转型的下一阶段成为可能。
真正的伙伴关系
在我们的对话中,最突出的一点是两家公司谈论其合作的方式。几乎没有传统供应商与客户关系的意味。相反,他们描述的是一段共同旅程,其中技术和使用技术的组织都在持续演进。
这种方法从项目一开始就很明显。在承诺更广泛推广之前,Apator 有机会在真实生产环境中测试该技术。这反映了 AiRob 与客户合作的更广泛方式。该公司不依赖销售演示或在受控实验室条件下进行的展示,而是更愿意在生产现场检验其技术。对双方而言,
At the same time, growing product complexity, increasingly demanding customer requirements and constant pressure to maintain the highest quality standards are forcing manufacturers to rethink how they organise their operations. And they must do so without compromising quality. Quite the opposite: the goal is to improve it even further.
It is hardly surprising, then, that manufacturers are increasingly exploring solutions based on artificial intelligence, alongside other forms of automation and production support. Yet the most interesting aspect is not necessarily the technology itself. What is far more revealing is how organisations arrive at the decision to implement it in the first place.
Discussions about AI tend to focus on return on investment, automation and cost reduction. Much less attention is paid to what implementing these technologies actually looks like in practice. What prompts a company to take the first step? What determines whether a project succeeds? And at what point does a technology cease to be an experiment and become an integral part of a factory's daily operations?
The story of Apator, a Polish manufacturer of metering devices and systems with a history dating back to 1949, illustrates that the answers to these questions begin to take shape long before a company invests in a new solution. In this case, it all started with a visit to another manufacturing facility.
Understanding the real needs is key
The Apator team visited another major Polish manufacturer, Fideltronik, at its facility in Sucha Beskidzka, to see how the company was using technologies to support electronics manufacturing. However, they did not arrive with a predefined list of requirements or a particular supplier in mind. The primary goal was to gain inspiration and compare their own experiences with the practices of another highly successful manufacturer.
As Sławomir Migała, Operations Director at Apator, explains in an interview with Evertiq, this approach is becoming increasingly essential.
Sławomir Migała | © Apator
"When exploring new areas of technology, you have to recognise that you cannot see every possibility from within your own factory. That is why we benchmark ourselves against others, look at best practices and assess how they might fit our own needs."
This observation captures a philosophy that ran throughout our conversation. In a world where technology is advancing faster than production management textbooks can keep up, competitive advantage increasingly belongs not to those who already have all the answers, but to those who know how to ask the right questions.
It was during the visit to Fideltronik that the Apator team came across a solution being implemented and further developed by another Polish company, AiRob. What caught their attention was not simply the fact that it used artificial intelligence, a technology now routinely presented as an essential part of modern manufacturing. The reason was far more practical.
"We were looking for solutions that would help our operators build quality into the process right at the workstation," Migała recalls. "We wanted a system that would be fast, require minimal involvement from our engineers, make it easy to create inspection plans and, at the same time, not compromise production throughput. When we saw the solution in action, we realised it addressed many of our needs."
That was the moment when technology stopped being an abstract concept and became a practical answer to specific manufacturing challenges.
The problem comes first, the technology second
It is easy to assume that AI adoption begins with enthusiasm for new technological possibilities, fuelled by the growing pressure on manufacturers to embrace artificial intelligence. Yet our conversation with Apator reveals quite the opposite.
For the Polish manufacturer, the starting point was not AI itself, but a specific operational challenge. In certain processes, Apator needed a more effective way to support operators during quality inspections. At the same time, not every process justified the expense of full automation or major modifications to existing production lines. And it is worth remembering that the products in question are highly sophisticated and sensitive.
Apator was therefore looking for a way to provide better support to operators during assembly, improve process reliability and reduce the risk of errors in areas where full automation simply did not make economic sense.
"We were looking for a solution that would help operators build quality into the product right at the workstation. We wanted something that would be easy to implement, require minimal engineering resources and, at the same time, not compromise production throughput."
One of the company's new production lines provides a particularly good example.
Given the existing production volumes, fully automating the line would not have been economically viable. However, the customer expected every unit to undergo automated checks to verify that all required components were present, within a clearly defined inspection scope.
"At our current production volumes, fully automating the line simply wouldn't make economic sense. But during an audit, the customer asked us a very specific question: 'How can you guarantee that every product is complete?' That was when we decided to install the system at every workstation. This allowed us to achieve 100% inspection coverage without making a major capital investment," explains Sławomir Migała.
This is where the distinction between implementing technology and solving problems becomes particularly clear. The team was not looking for the most advanced solution on the market. They were looking for a tool that would fit their existing production processes.
"We don't want to introduce isolated solutions. Our goal is to create a comprehensive system that supports production as a whole. We operate on a three-shift basis, often around the clock. We know that people's concentration levels fluctuate throughout the day. That is why we see this kind of support as a natural part of the production process."
This distinction matters. The objective was never to replace operators or diminish their role. Quite the opposite: it was to create a working environment that would help maintain consistently high quality standards, even in processes where full automation is either impractical or financially unjustifiable.
From inspiration to the first step
Returning from the visit, Apator did not rush into a large-scale rollout. Instead, the company began analysing which processes would genuinely benefit most from the new solution. The team also set out to identify where the technology could deliver the greatest value and which areas should be prioritised for the initial pilot. A dedicated team from AiRob worked closely with Apator to help answer these questions.
As Migała emphasises, the partner's expertise played an important role. AiRob went beyond simply supplying the technology, helping Apator identify the areas where implementation was most likely to deliver tangible results.
As a result, the first cameras were installed at seven production workstations. But this was only the beginning of the project. The greater challenge lay in determining whether the technology could become a natural part of everyday production operations.
The real question was no longer simply whether the system worked. It was whether the organisation could learn to make effective use of it.
The pilot project
The first few weeks after the system went live brought neither dramatic announcements nor decisions to expand the project immediately.
At Apator, the team understood that answers would take time. Rather than rushing into a large-scale rollout, the company opted for a pilot project. Working alongside AiRob, the team identified the areas where the solution could deliver the greatest value and installed the first workstations equipped with an image-based inspection system.
Over the following months, the project progressed steadily. The team monitored not only the system's performance but also how it was being used across different areas of production. Only by observing it over time could they determine whether the solution was genuinely becoming part of the production process or simply remaining another piece of equipment at the workstation.
After approximately a year, it was time to assess the results.
As Sławomir Migała recalls, this was when the team began to draw conclusions that would never have emerged from short-term testing.
"We had three main areas where the system had been implemented, and each developed somewhat differently. We saw that individual teams were using the solution in different ways and working at different paces. This showed us that we needed people who would take responsibility for standardising the solution across the organisation."
At first glance, this might seem like a purely organisational matter. In reality, it marked the beginning of the next phase of the project. The different experiences of individual teams demonstrated that further development would require common standards and clearly defined responsibilities.
"Today, we have a dedicated structure in place, with people responsible for developing and standardising the system. We are also seeing more and more new requirements and ideas for how the solution could be used. We don't want a series of standalone implementations. Our goal is to create a comprehensive system that supports production as a whole."
The project was thus beginning to move beyond individual implementations.
Perhaps the clearest indication of this shift was that new ideas were no longer coming exclusively from the team responsible for the project. Increasingly, production managers themselves were taking the initiative and proposing new applications for the technology.
This was arguably the real turning point, demonstrating that the system had begun to earn the trust of the wider organisation.
"Today, production managers come to us with their own ideas for further implementations. They can see the data, they can see the benefits, and they know which processes could gain the most from the solution. I think that's the best proof that the system has become a natural part of the way we work."
In many organisations, transformation is considered complete once a new technology is up and running. At Apator, however, that was only the beginning.
Good technology unlocks human potential
Although the initial goal of the implementation was to support quality inspection, over time Apator began to recognise the solution's much broader impact. In certain processes, the company had identified a risk of products leaving assembly incomplete, which needed to be addressed directly at the workstation. It became increasingly clear that the technology was also changing how people could make use of their time and expertise.
This is an aspect that receives relatively little attention in discussions about artificial intelligence, even though it was one of the original promises of the technology: to help people realise their full potential.
In many manufacturing companies, quality issues are addressed through additional training, new work instructions, changes to workstation layouts or modifications to product design. Each of these measures requires time, resources and the involvement of skilled personnel.
According to Sławomir Migała, advances in operator-assistance technologies are now making it possible to approach these challenges differently.
"In the past, whenever a quality issue arose, we would consider whether to provide additional operator training, revise the work instructions or redesign the product to eliminate the possibility of error. But all these measures take time and require the involvement of specialists. Today, we can resolve some of these issues much more quickly."
By taking over repetitive inspection tasks, the technology allows engineers and quality specialists to focus their expertise where it can deliver the greatest value.
"This allows us to devote more time to tasks that genuinely require knowledge and experience. Instead of focusing on recurring problems, we can work on improving our processes, analysing data and developing further improvements."
This approach aligns closely with the philosophy of continuous process improvement. Consistent quality control is not an end in itself. It provides the foundation on which an organisation can build the next stages of its development.
As Migała emphasises in our conversation, this is precisely why the project was viewed as a long-term investment from the outset.
"We don't want to develop individual solutions to individual problems. Our goal is to build an environment that will support our manufacturing operations, regardless of how our processes evolve."
A digital pair of eyes
One of the most interesting findings to emerge from the implementation was the system's impact on operators' day-to-day work.
Today, discussions about artificial intelligence often revolve around whether new technologies will replace human workers and how much financial value they can generate. At Apator, however, the conversation took a different direction from the outset. The question was how technology could help people perform their jobs with greater confidence, less stress and a lighter workload.
It is in this context that Migała describes the system as a "digital pair of eyes".
"No one can maintain the same level of concentration throughout an eight-hour shift. That's perfectly natural. So we see this system as an extra pair of eyes, supporting operators and helping them catch potential mistakes before a product leaves the workstation."
This approach also changes the way quality inspection is perceived. Traditionally, errors are often detected only at the end of the production process, when correcting them means additional work, delays or reworking a finished product. Here, corrective action can be taken immediately, with operators receiving feedback at the very moment they are performing the task.
Piotr Owczarek | © AiRob
As Piotr Owczarek, CEO of AiRob, explains, this immediate feedback is precisely what sets the solution apart from traditional quality inspection.
"If the system detects a missing component or another non-conformity, the operator can correct it straight away. There's no need to wait for final quality inspection or investigate a customer complaint. The problem is resolved right where it occurs," Owczarek explains.
Over time, this began to influence not only process quality but also the working environment and the atmosphere on the production floor.
"What mattered a great deal to me was that operators stopped seeing quality inspection as an assessment of their own performance. There's no inspector or auditor standing over their shoulder. Instead, they have a tool that helps them do the job correctly. That really changes the way people think about quality."
AiRob has observed similar effects in other implementations.
"On several occasions, operators have told us that the solution simply makes their work less stressful. They know that if they overlook something, the system will flag it immediately. That gives them a real sense of reassurance."
This is one of those benefits that is difficult to capture in a spreadsheet, yet can have a profound impact on both workplace culture and the quality of work across an entire team.
A Solution That Evolves with the Organisation
In many projects, getting a system up and running effectively marks the end of the implementation process. The customer receives a ready-made tool, and any subsequent development takes place largely independently of the facility where it is being used. In the case of Apator and AiRob, however, the experience gained on the production floor has become a driving force behind the continued development of the solution itself.
As Piotr Owczarek points out, every manufacturing facility brings its own requirements and production characteristics to the process.
"Every customer works a little differently, and every manufacturing operation has its own specific requirements. Of course, we come in with an existing solution, but it quickly becomes apparent that certain processes call for new features or a different approach. And that's when the most interesting part of our work begins."
A good example is the range of features that may now seem like obvious elements of the system but were actually introduced in response to requirements identified during successive implementations.
"Initially, the system couldn't read QR codes or text. It was our customers who showed us that these capabilities were needed in their processes. With each new implementation, the system gradually evolved."
Sławomir Migała sees the partnership in much the same way. When discussing AiRob, he places particular emphasis on the team's willingness to understand Apator's production processes and work together to find solutions.
"What really matters to me is that we're working with people who genuinely want to understand our manufacturing operations. They don't arrive assuming they already have all the answers. They listen first, then propose solutions, and work alongside us to see how those solutions perform in practice."
In this model, production processes are not forced to adapt to the limitations of an off-the-shelf tool. Instead, new requirements emerging on the production floor become the starting point for further development of the system.
Putting data to work
Initially, the system was primarily intended to support operators and assist with quality inspection. Over time, however, its role began to extend well beyond verifying assembly accuracy. Increasingly, it was not just the inspection results that mattered, but also the data the system collected. Each product, every operation performed, every correction made and every successful inspection provides only limited insight on its own. Taken together, however, these records build a detailed picture of the entire production process.
"Today, we see this solution from a much broader perspective. It's no longer just a quality inspection system. It has become another valuable source of information about our processes. The more we know about what's happening on the production floor, the easier it becomes to make informed decisions about future improvements," says Sławomir Migała.
For Piotr Owczarek, this represents one of the most natural directions in which such technologies can evolve.
"Every operation leaves a trace. We can go back to a product manufactured several months ago and reconstruct its entire assembly process. We can see who performed each operation, where non-conformities occurred and how they were resolved. This opens up entirely new possibilities for process analysis."
For Apator, this means being able to make decisions based on actual production data rather than relying solely on observations or isolated incidents. And it is precisely this kind of foundation that makes the next stages of digital transformation possible.
A true partnership
One of the things that stood out most during our conversation was the way both companies spoke about their collaboration. There was little sense of a traditional supplier-customer relationship. Instead, they described a shared journey in which both the technology and the organisation using it continued to evolve.
This approach was evident from the very beginning of the project. Before committing to a wider rollout, Apator had the opportunity to test the technology in a real production environment. This reflects AiRob's broader approach to working with customers. Rather than relying on sales presentations or demonstrations conducted under controlled laboratory conditions, the company prefers to put its technology to the test on the production floor. For both parties, what mattered was whether the solution could meet the demands of specific processes and overcome real manufacturing challenges.
"We don't want to convince customers with presentations or brochures alone. We'd rather demonstrate our solutions in the environment where they will actually be used. Only then can you properly assess whether they will deliver the expected results," explains Piotr Owczarek.
For AiRob, a pilot project is not simply an opportunity to evaluate an existing product in a particular production environment. It is also a chance to further develop the technology itself.
"Every implementation teaches us something. Customers introduce us to processes we haven't encountered before. And it's often during these projects that ideas for new features or improvements to the system emerge."
A good example is the range of features that may seem obvious today but were not part of the earliest versions of the solution.
"Initially, our system couldn't read QR codes or text. It was our customers who showed us that these capabilities were needed in their processes. That's how we came to develop additional modules for the system."
In practice, this means that the development of the solution does not end once it has been implemented. Quite the opposite: implementation marks the beginning of a new phase, one that helps shape the direction of future development.
This is precisely the kind of partnership that Apator particularly values.
"What really matters to me is having people on the other side who want to solve problems together with us. They don't arrive with a ready-made answer to every question. They first take the time to understand the process and only then propose the right solution," emphasises Sławomir Migała.
This approach reflects the philosophy behind the entire project. The aim was never simply to introduce another tool, but to build a solution capable of evolving alongside the organisation itself. And isn't that exactly what a true partnership in modern manufacturing should be about?
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